Injury Prevention Initiative for Africa: achievements and challenges.
Bibliographic record
Abstract
INTRODUCTION: We would be most grateful if you brought to the attention of the readers of African Health Sciences, the following information for IPIFA. The Injury Prevention Initiative for Africa (IPIFA) ratified its constitution at the fourth Annual General Meeting in February 2001. At that meeting, members from 8 African countries, and Associate members present, chose 9 representatives to constitute the IPIFA steering committee. The countries represented were Egypt, Ethiopia, Kenya, Mozambique, South Africa, Uganda, Zambia and Zimbabwe. The executive was re-elected: Erastus Njeru (Kenya), President; Olive Kobusingye (Uganda) Secretary General, and Fatma Hassan (Egypt) Treasurer. The Injury Control Center Uganda (ICC-U) was designated as the IPIFA secretariat and IPIFA was registered as an NGO in Uganda in 2002. The objectives outlined in the IPIFA constitution are to conduct and support research in injury control and promote safety; to develop and conduct training programmes in injury prevention and acute trauma care; to undertake advocacy for the prevention and control of injuries to influence the population and leadership; to mobilize local and international resources and to facilitate exchange of knowledge and experience, all in Africa. IPIFA will also act as a liaison for Africa with international and other continental stakeholders in injury control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".